How do neural networks and transformers work?

How do neural networks and transformers work?

A neural network processes numerical inputs through layers of calculations. Each layer combines values using learned weights, applies transformations, and passes a new representation onward. Training adjusts those weights so that the network performs a task more successfully.

For example, a network might transform image pixels into scores for different object categories. A language model transforms token representations into scores for possible next tokens.

What makes a transformer different?

A transformer is a particular neural-network architecture. One of its central mechanisms is attention, which lets representations combine information from relevant positions in a sequence.

Consider the sentence, "Riya placed the glass on the table because it was empty." Interpreting "it" requires considering surrounding words and possible relationships. Attention provides a computational mechanism for combining that context; it does not guarantee a correct interpretation.

Transformers also need information about position or order. The phrases "dog bites person" and "person bites dog" contain the same words but communicate different events.

Layers build contextual representations

Transformer blocks typically combine attention with feed-forward transformations. Stacking these blocks allows the model to repeatedly refine its internal representations.

The original transformer paper introduced an attention-based architecture for sequence tasks and demonstrated greater training parallelism than the recurrent approaches it compared against.

Learning and generation are different stages

During training, parameters change in response to prediction errors. During generation, the model normally uses fixed parameters to process the current context and select output tokens.

A transformer is therefore neither a database of complete answers nor a literal digital brain. It is an architecture for learned computation, whose useful behavior depends on its training and the application built around it.